How to interpret a decision tree in python. Step 5: (sort of optional) Optimizing the hyperparameters.

I've converted those variables to boolean and still getting the same results - the same headers like if A python library for decision tree visualization and model interpretation. tree_. Dec 7, 2020 · The final step is to use a decision tree classifier from scikit-learn for classification. com/l/tzxohThis webinar Once you've fit your model, you just need two lines of code. With 1. Supervised: The class of training set MUST be provided by the users. Decision trees are a supervised machine learning model used for both classification and regression tasks (CART). The Yes column contains the ID of the yes-branch, and the No column of the no-branch. In DecisionTreeClassifier, this pruning technique is parameterized by the cost complexity parameter, ccp_alpha. Classification and Regression Trees or CART for short is a term introduced by Leo Breiman to refer to Decision Tree algorithms that can be used for classification or regression predictive modeling problems. Feb 3, 2020 · When using a discrete classifier like decision tree, we get a single point (FPR, TPR) by through the confusion matrix, now when I try to plot ROC AUC curve, I get thresholds : roc_curve(y_test,mod. Limitations of Decision Tree Algorithm. Let’s start with the former. Classically, this algorithm is referred to as “decision trees”, but on some platforms like R they are referred to by Mar 2, 2019 · To demystify Decision Trees, we will use the famous iris dataset. tree import DecisionTreeClassifier from sklearn import tree classifier = DecisionTreeClassifier(max_depth = 3,random_state = 0) tree. 1: If user-acceleration-magnitude-mean is less or equal than 0. Decision Node: They are the nodes Nov 28, 2023 · Introduction. A decision tree split the data into multiple sets. In this article, I will walk you through the Algorithm and Implementation of…. NOTE: You can support StatQuest by purchasing the Jupyter Notebook and Python code seen in this video here: https://statquest. Pre-Pruning is considered more efficient and effective as it Dec 2, 2016 · For example, @user1808924 mentioned in his answer; one rule which is representing the left-most branch of your tree model. Feb 5, 2022 · For the first decision tree, it may choose only feature 1 and feature 2; For the second decision tree, it uses the different pair of features, e. Plot the decision surface of a decision tree trained on pairs of features of the iris dataset. In Python, we can use the scikit-learn method DecisionTreeClassifier for building a Decision Tree for classification. The dataset contains information for three classes of the IRIS plant, namely IRIS Setosa, IRIS Versicolour, and IRIS Virginica, with the following attributes: sepal length, sepal width, petal length, and petal width. Each node that is not a leaf (root or branch) splits its part of the data in two sub-parts. Aug 23, 2016 · Returns the mean accuracy on the given test data and labels. Using Python. ensemble import RandomForestClassifier. The decision tree to be plotted. The next Dec 24, 2019 · As you can see, visualizing decision trees can be easily accomplished with the use of export_graphviz library. A decision tree begins with the target variable. 3 Decision Tree interpretation evaluation: Nov 16, 2023 · In this section, we will implement the decision tree algorithm using Python's Scikit-Learn library. Overall feature importance of a decision tree can be calculated in the following way. clf = tree. Nov 2, 2022 · Flow of a Decision Tree. The options are “gini” and “entropy”. tree import DecisionTreeClassifier import matplotlib. Jul 31, 2019 · This tutorial covers decision trees for classification also known as classification trees. Your decision tree is treating the binary variable as a numeric variable, hence the representation if_successful <= 0. Each of its element arrays (which corresponds to a tree node) is of length equal to the number of classes (here 3) Each of these 3-element arrays corresponds to the amount of training samples that end up in the respective node for each class. pyplot as plt import matplotlib. Decision trees are intuitive, easy to interpret, and can handle both numerical and categorical data. If None, the tree is fully generated. 21 or newer. export_graphviz(dtr. Bonus Step 6: Visualizing the decision tree. # Step 1: Import the model you want to use. Let’s start by creating decision tree using the iris flower data se t. A decision tree is a tree-like structure that represents a series of decisions and their possible consequences. These classifiers build a sequence of simple if/else rules on the training data through which they predict the target value. More than Feb 25, 2021 · Extract Code Rules. Additionally, this tutorial will cover: The anatomy of classification trees (depth of a tree, root nodes, decision nodes, leaf nodes/terminal nodes). Apr 19, 2023 · Decision tree is a type of algorithm in machine learning that uses decisions as the features to represent the result in the form of a tree-like structure. Here we fetch the best estimator obtained from the gridsearchcv as the decision tree classifier. When our goal is to group things into categories (=classify them), our decision tree is a classification tree. Nov 7, 2022 · Decision Tree Algorithm in Python. Step 5: (sort of optional) Optimizing the hyperparameters. Greater values of ccp_alpha increase the number of nodes pruned. The code uses only NumPy, Pandas and the standard…. Impurity-based feature importances can be misleading for high cardinality features (many unique values). Assume that our data is stored in a data frame ‘df’, we then can train it Nov 22, 2021 · Classification and Regression Trees (CART) can be translated into a graph or set of rules for predictive classification. We are only interested in first element of the list. #train classifier. 7. dot file is and enter this command line: dot -T png treepic. May 22, 2024 · Pruning Techniques. You'll also learn the math behind splitting the nodes. From the analysis perspective the first node is the root node, which is the first variable that splits the target variable. Previously, I had explained the various Regression models such as Linear, Polynomial and Support Vector Regression. In multi-label classification, this is the subset accuracy. g. (This is continued downwards into the tree. – Downloading the dataset Interpreting Decision Trees. decision_path(X_test) # Similarly, we can also have the leaves ids reached by each sample. – Preparing the data. One of them is ID3 (Iterative Dichotomiser 3) and we are going to see how to code it from scratch using ONLY Python to build a Decision Tree Classifier. Go through all splits and pay attention to how much each feature split reduces the variance(for regression) or Gini index(for classification) compared to the parent node. pyplot as plt. The final node (leaf node) will provide the predicted class or value. 📚 Programming Books & Merch 📚🐍 The Python Bible Book: https://www. All the code can be found in a public repository that I have attached below: Dec 16, 2019 · Step #2: Import Packages and Read the Data. Coding a regression tree I. trees_to_dataframe(). This algorithm can be used for regression and classification problems — yet, is mostly used for classification problems. Step 2. A non zero element of. They expect you to provide the most crucial tree (a single decision tree), which is defined as the "best_tree" variable in our example above. tree import DecisionTreeClassifier# Step 2: Make an instance of the Model. It is a tree-based algorithm that divides the entire dataset into a tree-like structure based on certain conditions. Nov 18, 2020 · Contoh: Baca dan cetak kumpulan data. If it Feb 4, 2021 · Here, I've explained how to solve a regression problem using Decision Trees in great detail. import pandas from sklearn import tree import pydotplus from sklearn. Jul 1, 2018 · The decision_path. Implementing a decision tree in Weka is pretty straightforward. When we use a decision tree to predict a number, it’s called a regression tree. Hands-On Machine Learning with Scikit-Learn. In this tutorial, you’ll learn how the algorithm works, how to choose different parameters for your model, how to… Read More »Decision Tree Classifier with Jan 19, 2016 · I am using sk-learn python 27 and have output some decision tree feature results. js. Splitting: Process of dividing node into one or more sub-nodes based on some split criteria. May 15, 2020 · Am using the following code to extract rules. from sklearn import tree. There is no single decision tree algorithm. music_d=pd. tree import DecisionTreeClassifier. May 17, 2024 · A decision tree is a flowchart-like structure used to make decisions or predictions. data = load_iris() Feb 16, 2022 · Let’s code a Decision Tree (Classification Tree) in Python! Coding a classification tree I. Please help me plot a tree of higher resolution as the image gets blurred when I increase the tree depth. 973, follow True. Though I am not sure how to interpret the results. Here we only show the effect of ccp_alpha on regularizing the trees and how to choose a Jan 22, 2023 · Step 2: Prepare the dataset. The iris data set contains four features, three classes of flowers, and 150 samples. There isn't any built-in method for extracting the if-else code rules from the Scikit-Learn tree. May 16, 2018 · In this article, we will talk about decision tree classifiers and how we can dynamically visualize them. Feature importances are provided by the fitted attribute feature_importances_ and they are computed as the mean and standard deviation of accumulation of the impurity decrease within each tree. Yes, your interpretation is correct. 3 on Windows OS) and visualize it as follows: from pandas import Jul 12, 2023 · This is the new ‘decision node’. Decision Tree is one of the powerful algorithms that come under the non-parametric Supervised Learning Technique. Second, create an object that will contain your rules. The sklearn needs to be version 0. Setting Up Your Python Environment. In general, the rules have the form: May 4, 2018 · You can find the decision rules as a dataframe through the function model. They do not demand much preprocessing of the training data, and can intrinsically handle categorical features and missing values. It can be an instance of DecisionTreeClassifier or DecisionTreeRegressor. Python Nov 24, 2023 · Advantages of decision trees: Decision trees are interpretable and intuitive since one can get a clear idea of what led to a specific prediction. Then each of these sets is further split into subsets to arrive at a decision. tree import _tree. It is used in machine learning for classification and regression tasks. Decision tree pruning plays a crucial role in optimizing decision tree models by preventing overfitting, improving generalization, and enhancing model interpretability. dot', feature_names=X. Jan 6, 2023 · Fig: A Complicated Decision Tree. In the following examples we'll solve both classification as well as regression problems using the decision tree. We will also be discussing three differe Dec 13, 2020 · In that article, I mentioned that there are many algorithms that can be used to build a Decision Tree. export_dict() function seems to be exactly what I'm looking for, but I can't figure out how to call it (keep getting an AttributeError: 'module' object has no attribute 'export_dict'). value is an array of arrays, of length equal to the number of nodes in the tree. Unlike an actual tree, the decision tree is displayed upside down with the “leaves” located at the bottom, or foot, of the tree. We’ll use the zoo dataset from Tomi Mester’s previous pandas tutorial articles. prediction = clf. Decision trees are versatile machine learning algorithm capable of performing both regression and classification task and even work in case of tasks which has multiple outputs. clf = DecisionTreeClassifier(max_depth = 2, random_state = 0)# Step 3: Train the model on the data. We will be using the IRIS dataset to build a decision tree classifier. Though, setting up grahpviz itself could be a quite tricky task to do, especially on Windows machines. The primary appeal of decision trees is that they can be displayed graphically as a tree-like graph, and they’re easy to explain to non-experts. With the rise of the XGBoost library, Decision Trees have been some of the Machine Learning models to deliver the best results at competitions. An example of a decision tree is a flowchart that helps a person decide what to wear based on the weather conditions. See decision tree for more information on the estimator. _Booster. When interpreting a decision tree, start at the root node and traverse the tree by following the decision rules that apply to the input data. feature 3 and feature 1; and so on… 3 Advantages and 3 disadvantages of decision trees in your project. clf=clf. columns) then open up command prompt where the treepic. Jan 1, 2023 · Final Decision Tree. Oct 19, 2021 · Root Node: This represents the topmost node of the tree that represents the whole data points. 3, we now provide one- and two-dimensional feature space illustrations for classifiers (any model that can answer predict_probab() ); see below . import matplotlib. drop(columns=['genre']) y=music_d['genre'] model=DecisionTreeClassifier() Mar 4, 2024 · The role of categorical data in decision tree performance is significant and has implications for how the tree structures are formed and how well the model generalizes to new data. predict_proba(X) is: The predicted class probability which is the fraction of samples of the same class in a leaf. def tree_to_code(tree, feature_names): tree_ = tree. dot -o treepic. This way you can reconstruct the tree, since for each row of the dataframe, the node ID has directed edges to Yes and No. Oct 27, 2021 · Inexpensive to construct with an easy to interpret logic. Tree depth isn't an issue in my case, since I've set max_depth = 2 – In this tutorial, learn Decision Tree Classification, attribute selection measures, and how to build and optimize Decision Tree Classifier using Python Scikit-learn package. tree_, out_file='treepic. each label set be correctly predicted. My question is, 1) are my results reflective of my subject? The fact that KNN accuracy is 94% and Decision Tree of 48% is confusing. 2. There are two main types of pruning: Pre-Pruning (Early Stopping): Stops the tree growth early by setting constraints during the construction phase. 1%. (graph, ) = pydot. Non-parametric: Decision tree does NOT make assumptions about data’s distribution or structure. from sklearn. Aug 20, 2021 · Creating and visualizing decision trees with Python. They help when logistic regression models cannot provide sufficient decision boundaries to predict the label. If None, generic names will be used (“x[0]”, “x[1]”, …). Decision trees use both classification and regression. Tour Start here for a quick overview of the site Help Center Detailed answers to any questions you might have . Iris species. Disadvantages of decision trees: Decision trees are sensitive to small Gather the data. Python Decision-tree algorithm falls under the category of supervised learning algorithms. They are also the fundamental components of Random Forests, which is one of the May 29, 2022 · Today we learn how to visualize decision trees in Python. fit(new_data,new_target) # train data on new data and new target. # This was already imported earlier in the notebook so commenting out. csv') X=music_d. An array containing the feature names. Finally, select the “RepTree” decision Feb 12, 2022 · 0. Q2. There are three of them : iris setosa, iris versicolor and iris virginica. Decision trees are simple to interpret due to their structure and the ability we have to visualize the modeled tree. So, in short: The tree can be linearized into decision rules, where the outcome is the contents of the leaf node, and the conditions along the path form a conjunction in the if clause. It works for both continuous as well as categorical output variables. DecisionTreeClassifier() # defining decision tree classifier. which is a harsh metric since you require for each sample that. com Aug 22, 2023 · Classification using Decision Tree in Weka. 002. Aug 26, 2020 · Important terms used in Decision Tree Root Node: The topmost node of the tree. So, while this method of visualization is not the worst, we must Aug 10, 2021 · DECISION TREE (Titanic dataset) A decision tree is one of most frequently and widely used supervised machine learning algorithms that can perform both regression and classification tasks. Refresh the page, check Medium ’s site status, or find something interesting to read. Apr 10, 2024 · Conclusion. compare the Gini impurity score, after n before using new attribute to separate data. Each branch represents the outcome of a decision or variable, and Aug 27, 2021 · Decision trees in Python. export_graphviz(clf, out_file=your_out_file, feature_names=your_feature_names) Hope it works, @Matt Jul 19, 2021 · Timestamps0:00 - 0:23 Intro0:23 - 0:55 What Does A Decision Tree Look Like?0:56 - 1:50 A Deep Dive Into Our Dataset1:51 - 2:26 How do Decision Trees Come Up May 31, 2024 · A. ----------. The code below first fits a random forest model. I am trying to design a simple Decision Tree using scikit-learn in Python (I am using Anaconda's Ipython Notebook with Python 2. At first, I thought the features are listed from the most informative to least informative (from top to bottom), but examining the \nvalue it suggests otherwise. Jun 4, 2021 · Visualize the decision tree with Graphviz using the scikit-learn export_graphviz function: sklearn. # method allows to retrieve the node indicator functions. Some of its deterrents are as mentioned below: decision_tree decision tree regressor or classifier. Aug 15, 2023 · The Decision Tree algorithm will learn patterns and decision rules based on the features to classify transactions as either fraudulent or legitimate. Want to learn more? Take the full course at https://learn. tree import export_text. Model Training: Train the Decision Tree model on the training data, using a suitable metric such as Information Gain or Gini Impurity to determine the best feature to split the data at each node. Divide the dataset into the subsets based on the possible values of the selected attribute (in Step 2) Repeat the above steps for all the subsets created until Apr 14, 2021 · Apologies, but something went wrong on our end. To add to Lauren's answer: based on PUBDEV-4324 - Expose Decision Tree as a stand-alone algo in H2O both DRF and GBM can do the job with GBM being marginally easier: titanic_1tree = h2o. Warning. datacamp. predict(iris. # through the node j. # indicator matrix at the position (i, j) indicates that the sample i goes. Apr 5, 2020 · Main point when process the splitting of the dataset. It predicts class probabilities - the node values. Feb 8, 2022 · A Decision Tree follows a tree-like structure (hence the name) whereby a node represents a specific attribute, a branch represents a decision rule, and leaf nodes represent an outcome. Jun 5, 2019 · Predict in the Decision Tree is simply to follow the path in the constructed tree-shape decisions to the leaf node, and return the value of that node as we define in the fit() function. class_namesarray-like of shape (n_classes Jul 3, 2015 · tree. node_indicator = estimator. Decision Tree for Classification. Feb 5, 2020 · Decision Tree. Or you can directly use the embedded function: tree. Click the “Choose” button. The decision tree estimator to be exported. feature_names array-like of str, default=None. gumroad. neuralnine. 1: Addressing Categorical Data Features with One Hot Encoding. You can do that with networkx Mar 6, 2022 · The interpretation is easy. Root/branch node: 1. 1. While creating a decision tree, the key thing is to select the best attribute from the total features list of the dataset for the root node and for sub-nodes. Feb 11, 2016 · 2. 5. Next, let’s read in the data. The result of clf. They are powerful algorithms, capable of fitting even complex datasets. Features: sepal length (cm), sepal width (cm), petal length (cm), petal width (cm) Numerically, setosa flowers are identified by zero, versicolor by one, and Apr 17, 2022 · In this tutorial, you’ll learn how to create a decision tree classifier using Sklearn and Python. import pandas as pd. Feb 1, 2022 · One more thing. Cost complexity pruning provides another option to control the size of a tree. graph_from_dot_data(dot_data. 5: the successor of ID3 2. Jan 12, 2021 · Decision Tree Algorithms. png file should be created with your decision tree. We’ll plot feature importances obtained from the Decision Tree model to see which features have the greatest predictive power. A Decision Tree can be used for Regression and Classification tasks alike. They are easy to implement, explain and are among the Mar 28, 2024 · Building Your First Decision Trees in Python. Splitting: It refers to dividing a node into two or more sub-nodes. In this article, We are going to implement a Decision tree in Python algorithm on the Balance Scale Weight & Distance Apr 7, 2016 · Decision Trees. The Decision Tree then makes a sequence of splits based in hierarchical order of impact on this target variable. Oct 4, 2019 · 0. Just Re-install Anaconda with the latest version and use this code: import pandas as pd. #from sklearn. Interpreting our model with confidence The combination of a solid theoretical justification and a fast practical algorithm makes SHAP values a powerful tool for confidently interpreting tree May 22, 2020 · clf. From the drop-down list, select “trees” which will open all the tree algorithms. A decision tree follows a set of if-else conditions to visualize the data and classify it according to the conditions. This dataset is made up of 4 features : the petal length, the petal width, the sepal length and the sepal width. We need to write it. The maximum depth can be specified in the XGBClassifier and XGBRegressor wrapper classes for XGBoost in the max_depth parameter. export_graphviz Lastly, the most efficient method of visualizing trees with the dtreeviz Feb 23, 2019 · A Scikit-Learn Decision Tree. For each pair of iris features, the decision tree learns decision boundaries made of combinations of simple thresholding rules inferred from the training samples. Building a Decision Tree in Python demystifies the process of data analysis and machine learning, making it accessible even to beginners. max_depth int, default=None. The root node contains all data (from the training set). plot_tree(classifier); Jul 2, 2024 · Decision Tree visualization facilitates interpretation and comprehension of the model’s choices. Interpretation of the results: The first print returns ['male' 'male'] so the data [[68,9],[66,9]] are predicted as males. Note, that scikit-learn also provides DecisionTreeRegressor, a method for using Decision Trees for Regression. Decision trees, being a non-linear model, can handle both numerical and categorical features. It’s only a few rows (22) but will be perfect to learn how to build a classification tree with scikit-learn. png A . I thought accuracy of decision tree would be higher. To make the rules look more readable, use the feature_names argument and pass a list of your feature names. A decision tree is one of the supervised machine learning algorithms. Cast the variable to binary or boolean and train the model and it will work fine and give you a 0 or 1 split. This is usually called the parent node. read_csv ("shows. tree. We will show this structure later so you can see what we mean but you can imagine it is like one of the decision trees you used to draw in high school maths Sep 9, 2022 · In the "dtreeviz" library, the approach is to identify the most important decision trees within the ensemble of trees in the XGBOOST model. image as pltimg df = pandas. Let’s change a couple of parameters to see if there is any effect on the accuracy and also to make the tree shorter. In this article, we’ll create both types of trees. In this tutorial, we will focus on building a Decision Tree Regressor using Python and the scikit-learn library. This section guides you through creating your first Decision Tree using Python, emphasizing practical experience and clarity. Jul 14, 2020 · Apologies, but something went wrong on our end. Decision trees are an intuitive supervised machine learning algorithm that allows you to classify data with high degrees of accuracy. read_csv('music. See Permutation feature importance as Oct 20, 2016 · After you fit a random forest model in scikit-learn, you can visualize individual decision trees from a random forest. ) 2: I googled a bit, and found the "gini coefficient: a statistical measure of the degree of variation represented in a set of values, used especially in analysing income inequality". getvalue()) 2) Or collect entire list in graph but just use first element to be sent to pdf. First, let’s import some functions from scikit-learn, a Python machine learning library. If None generic names will be used (“feature_0”, “feature_1”, …). Instead, multiple algorithms have been proposed to build decision trees: ID3: Iterative Dichotomiser 3; C4. First, import export_text: from sklearn. In addition, decision tree models are more interpretable as they simulate the human decision-making process. datasets import load_breast_cancer. Step 3: Training the decision tree model. calculate all of the Gini impurity score. X : array-like, shape = (n_samples, n_features) Test samples. Furthermore, this is a classification tree. Parameters. So you can do this one of following of two ways, 1) Change line where you collect dot_data value in graph to. If you just installed Anaconda, it should be good enough. Nov 27, 2017 · A decision tree is a binary tree where each node represents a portion of the data. Key components to look for when interpreting a decision tree include: Decision Rule: The condition used Aug 31, 2017 · type(graph) <type 'list'>. Machine Learning and Deep Learning with Python Nov 13, 2017 · 7. Step 4: Evaluating the decision tree classification accuracy. The treatment of categorical data becomes crucial during the tree Jan 1, 2020 · Simple decision tree with a max depth of 2 and accuracy of 79. gbm(x = predictors, y = response, training_frame = titanicHex, ntrees = 1, min_rows = 1, sample_rate = 1, Apr 17, 2018 · Tree SHAP is a fast algorithm that can exactly compute SHAP values for trees in polynomial time instead of the classical exponential runtime (see arXiv). In this decision tree plot tutorial video, you will get a detailed idea of how to plot a decision tree using python. Aug 27, 2020 · Generally, boosting algorithms are configured with weak learners, decision trees with few layers, sometimes as simple as just a root node, also called a decision stump rather than a decision tree. Max_depth: defines the maximum depth of the tree. Though the Decision Tree classifier is one of the most sophisticated classification algorithms, it may have certain limitations, especially in real-world scenarios. 2: Splitting the dataset. csv") print (df) Untuk membuat pohon keputusan, semua data harus berupa numerik. Each internal node corresponds to a test on an attribute, each branch Aug 23, 2023 · Decision trees are powerful machine learning algorithms that can be used for both classification and regression tasks. 2) I am especially unsure if my column features for both KNN and Decision Tree are the same, to reflect the same result. Apr 15, 2020 · Scikit-learn 4-Step Modeling Pattern. It is a common tool used to visually represent the decisions made by the algorithm. Apr 26, 2022 · Decision tree is a non-parametric, supervised, classification algorithm that assigns data to discrete groups. Regression trees are used when the dependent variable is Feb 6, 2024 · Decision Tree is one of the most powerful and popular algorithms. Pruning is essential to avoid overfitting and improve the generalizability of the decision tree. data[removed]) # assign removed data as input. Just complete the following steps: Click on the “Classify” tab on the top. Dec 9, 2019 · Decision Tree result. Jan 21, 2019 · The sklearn. The aim of this article is to make all the parts of a decision tree classifier clear by walking through the code that implements the algorithm. Criterion: defines what function will be used to measure the quality of a split. You can do something like the following: Theory. The target variable to predict is the iris species. feature_namesarray-like of shape (n_features,), default=None. The maximum depth of the representation. com/courses/machine-learning-with-tree-based-models-in-python at your own pace. A: It reduces the possibility of overfitting, as the decision trees are based on subsets First export the tree to the JSON format (see this link) and then plot the tree using d3. Decisions Trees is a powerful group of supervised Machine Learning models that can be used for both classification and regression. The code below is based on StackOverflow answer - updated to Python 3. Note: Both the classification and regression tasks were executed in a Jupyter iPython Notebook. This tree seems pretty long. Maximum Depth: Limits the depth of the tree. Each level in your tree is related to one of the variables (this is not always the case for decision trees, you can imagine them being more general). It consists of nodes representing decisions or tests on attributes, branches representing the outcome of these decisions, and leaf nodes representing final outcomes or predictions. The selection of best attributes is being achieved with the help of a technique known as the Attribute Selection Measure (ASM). Currently supports scikit-learn , XGBoost , Spark MLlib , and LightGBM trees. Recommended books. X has medium income, so you go to Node 2, and more than 7 cards, so you go to Node 5. Names of each of the features. Post-Pruning is used generally for small datasets whereas Pre-Pruning is used for larger ones. ew gs pe pn jy iv kd wx cv ct